Recovery from a stroke or a spinal cord injury can be difficult and can sometimes be a life-long process. Many patients (also referred to herein as “subjects” or “humans”) can improve their motion functions after receiving carefully directed physical training. In the rehabilitation programs, patients may be trained to sit, stand, and walk. The training can substantially help the patients regain their strength and endurance. Typically, patients go to hospitals or other medical facilities for rehabilitation, where medical professionals (e.g., nurses and/or therapists) guide the patients. The medical professionals are needed to observe the progress and correct the patients' gestures. Thus, rehabilitation costs can be very expensive. For example, the cost of physical therapy for stroke patients in the United States is about $28 billion per year (see e.g., Stroke Statistics, available at http://www.uhnj.org/stroke/stats.htm), and this number keeps going up since more and more people are having strokes today due to work stress, eating styles, and environmental factors.
Intelligent rehabilitation systems that reduce the cost of treatment have been proposed. These intelligent rehabilitation systems deploy known sensing systems, as well as machine learning techniques. Examples of sensing systems deployed in these intelligent rehabilitation systems include wearable, inertial sensors (I. P. I. Pappas, T. Keller, S. Mangold, M. R. Popovic, V. Dietz, M. Morari, “A reliable gyroscope-based gait-phase detection sensor embedded in a shoe insole,” IEEE Sensors Journal, vol. 4, no. 2, pp. 268-274, April 2004; Y. L. Hsu, P. C. Chung, W. H. Wang, M. C. Pai, C. Y. Wang, C. W. Lin, H. L. Wu, J. S. Wang, “Gait and Balance Analysis for Patients With Alzheimer's Disease Using an Inertial-Sensor-Based Wearable Instrument,” IEEE Journal of Biomedical and Health Informatics, vol. 18, no. 6, pp. 1822-1830, November 2014; S. R. Hundza, W. R. Hook, C. R. Harris, S. V. Mahajan, P. A. Leslie, C. A. Spani, L. G. Spalteholz, B. J. Birch, D. T. Commandeur, N. J. Livingston, “Accurate and Reliable Gait Cycle Detection in Parkinson's Disease,” IEEE Trans. Neural Syst. Rehabil. Eng., vol. 22, no. 1, pp. 127-137, Jan. 2014; W. Lee, S. Yen, A. Tay, Z. Zhao, T. M. Xu, K. K. M. Ling, Y. Ng, E. Chew, A. L. K. Cheong, and G. K. C. Huat, “A Smartphone-Centric System for the Range of Motion Assessment in Stroke Patients,” IEEE Journal of Biomedical and Health Informatics, vol. 18, no. 6, pp. 1839-1847, November 2014), video cameras (R. Ayase, T. Higashi, S. Takayama, S. Sagawa, and N. Ashida, “A method for supporting at-home fitness exercise guidance and at-home nursing care for the elders, video-based simple measurement system,” 10th IEEE Intl. Conf. on e-Health Networking, Applications and Service (HEALTHCOM 2008), pp. 182-186, July 2008), MICROSOFT KINECT of MICROSOFT CORP. of REDMOND, Wash. (I. Ar and Y. S. Akgul, “A Computerized Recognition System for the Home-Based Physiotherapy Exercises Using an RGBD Camera,” IEEE Trans. Neural Syst. Rehabil. Eng., vol. 22, no. 6, pp. 1160-1171, November 2014; C. D. Metcalf, R. Robinson, A. J. Malpass, T. P. Bogle, T. A. Dell, C. Harris and S. H. Demain, “Markerless Motion Capture and Measurement of Hand Kinematics: Validation and Application to Home-Based Upper Limb Rehabilitation,” IEEE Trans. Bio. Eng., vol. 60, no. 8, pp. 2184-2192, August 2013), and marker-based systems for detecting markers on human body joints (S. Das, L. Trutoiu, A. Murai, D. Alcindor, M. Oh, F. D. Torre, and J. Hodgins, “Quantitative measurement of motor symptoms in Parkinson's disease: A study with full-body motion capture data,” 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 6789-6792, August 2011).
Unlike other applications such as smart homes, which only require coarse recognition of the human motions, a sensing system for intelligent rehabilitation training applications must recognize the motion of specific limbs or body parts with finer resolution. Conventional intelligent rehabilitation system have some limitations. For example, wearable, inertial sensors (e.g., accelerometers, gyroscopes, magnetometers, etc.) can be deployed on the joints of the human body to capture the motion of limbs. Although inertial sensors are good at detecting dynamic motion signals, inertial sensors are incapable of capturing real human postures since inertial sensors do not capture images of the subject's body. Inertial sensors are also susceptible to accumulating displacement errors. Video cameras and depth sensors (such MICROSOFT KINECT) can capture images, and skeleton patterns can be extracted for later posture recognition. However, video/depth image processing is computationally expensive, particularly when extracting motion patterns. In addition, video cameras and depth sensors are limited by the illumination conditions, obstacles, and limited field of view. Although it may be possible to apply processing techniques to overcome some of these limitations, such processing techniques increase computation costs even more. Marker-based sensing systems can accurately detect human posture, but due to high costs, deployment in intelligent rehabilitation system applications is not feasible.